activity
20192024
most citedArtificial Intelligence for In Silico Clinical Trials: A Review

10 citations · 19 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2024

Signal Quality Auditing for Time-series Data

Chufan Gao, Nicholas Gisolfi, Artur Dubrawski

Signal quality assessment (SQA) is required for monitoring the reliability of data acquisition systems, especially in AI-driven Predictive Maintenance (PMx) application contexts. S…

cs.LG2023★ 1 cited

MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement

Zifeng Wang, Chufan Gao, Cao Xiao +1

Tabular data prediction has been employed in medical applications such as patient health risk prediction. However, existing methods usually revolve around the algorithm design whil…

q-bio.QM2022★ 10 cited

Artificial Intelligence for In Silico Clinical Trials: A Review

Zifeng Wang, Chufan Gao, Lucas M. Glass +1

A clinical trial is an essential step in drug development, which is often costly and time-consuming. In silico trials are clinical trials conducted digitally through simulation and…

cs.CL2022★ 2 cited

Classifying Unstructured Clinical Notes via Automatic Weak Supervision

Chufan Gao, Mononito Goswami, Jieshi Chen +1

Healthcare providers usually record detailed notes of the clinical care delivered to each patient for clinical, research, and billing purposes. Due to the unstructured nature of th…

cs.LG2021★ 5 cited

Learning Graph Neural Networks for Multivariate Time Series Anomaly Detection

Saswati Ray, Sana Lakdawala, Mononito Goswami +1

In this work, we propose GLUE (Graph Deviation Network with Local Uncertainty Estimation), building on the recently proposed Graph Deviation Network (GDN). GLUE not only automatica…

cs.LG2021

The Word is Mightier than the Label: Learning without Pointillistic Labels using Data Programming

Chufan Gao, Mononito Goswami

Most advanced supervised Machine Learning (ML) models rely on vast amounts of point-by-point labelled training examples. Hand-labelling vast amounts of data may be tedious, expensi…

cs.LG2019★ 1 cited

Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning

Chufan Gao, Fabian Falck, Mononito Goswami +3

Monitoring physiological responses to hemodynamic stress can help in determining appropriate treatment and ensuring good patient outcomes. Physicians' intuition suggests that the h…